Fog Computing for Intelligent Cloud IoT Systems E-Book
173,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Sprache: Englisch
FOG COMPUTING FOR INTELLIGENT CLOUD IOT SYSTEMS
This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services
Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources.
Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user’s experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service.
Fog computing has various applications across industries, such as agriculture and farming, the healthcare industry, smart cities, education, and entertainment. For example, in the agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing’s help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage.
This book is divided into three sections. The first section studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent Cloud-IoT systems, machine learning fundamentals, and data visualization. The second section focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This section also covers analytics in fog computing using big data and patient monitoring systems, and the emergence of fog computing concerning applications and potentialities in traditional and digital educational systems. Security aspects in fog computing through blockchain and IoT, and fine-grained access through attribute-based encryption for fog computing are also covered.
Audience
The book will be read by researchers and engineers in computer science, information technology, electronics, and communication specializing in machine learning, deep learning, the cyber world, IoT, and security systems.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 648
Veröffentlichungsjahr: 2024
Ähnliche
Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Part I: STUDY OF FOG COMPUTING AND MACHINE LEARNING
1 Fog Computing: Architecture and Application
1.1 Introduction
1.2 Fog Computing: An Overview
1.3 Fog Computing for Intelligent Cloud-IoT System
1.4 Fog Computing Architecture
1.5 Basic Modules of Fog Computing
1.6 Cloud Computing vs. Fog Computing
1.7 Fog Computing vs. IoT
1.8 Applications of Fog Computing
1.9 Will the Fog Be Taken Over by the Cloud?
1.10 Challenges in Fog Computing
1.11 Future of Fog Computing
1.12 Conclusion
References
2 A Comparative Review on Different Techniques of Computation Offloading in Mobile Cloud Computing
2.1 Introduction
2.2 Related Works
2.3 Computation Offloading Techniques
2.4 Conclusion
2.5 Future Scope
2.6 Acknowledgement
References
3 Fog Computing for Intelligent Cloud–IoT System: Optimization of Fog Computing in Industry 4.0
3.1 Introduction
3.2 How Fog Computing with IIoT Brings Revolution
3.3 Applications of Fog Computing on Which Industries Rely
3.4 Data Analysis
3.5 Illustration of Fog Computing and Application
3.6 Conclusion
3.7 Future Scope/Acknowledgement
References
4 Machine Learning Integration in Agriculture Domain: Concepts and Applications
4.1 Introduction
4.2 Fog Computing in Agriculture
4.3 Methodology
4.4 Results and Discussion
4.5 Conclusion
4.6 Future Scope
References
5 Role of Intelligent IoT Applications in Fog Computing
5.1 Introduction
5.2 Cloud Service Model’s Drawbacks
5.3 Fog Computation
5.4 Recompenses of FoG
5.5 Limitation of Fog Computing
5.6 Fog Computing with IoT
5.7 Edge AI Embedded
5.8 Network Intelligence Objectives
5.9 Farming with Fog Computation (Case Study)
5.10 Conclusion
References
6 SaaS-Based Data Visualization Platform—A Study in COVID-19 Perspective
6.1 Introduction
6.2 Summary of Objectives
6.3 What is a Pandemic?
6.4 COVID-19 and Information Gap
6.5 Data Visualization and its Importance
6.6 Data Management with Data Visualization
6.7 What is Power BI?
6.8 Output Data
6.9 Design & Implementation
6.10 Dashboard Development
6.11 Advantages and its Impact
6.12 Conclusion and Future Scope
References
7 A Complete Study on Machine Learning Algorithms for Medical Data Analysis
7.1 Introduction
7.2 Pre-Processing Medical Data for Machine Learning
7.3 Supervised Learning Algorithms for Medical Data Analysis
7.4 Unsupervised Learning Algorithms for Medical Data Analysis
7.5 Applications of Machine-Learning Algorithms in Medical Data Analysis
7.6 Limitations and Challenges of Machine Learning Algorithms in Medical Data Analysis
7.7 Future Research Directions and Machine Learning Developments in the Realm of Medical Data Analysis
7.8 Conclusion
References
Part II: APPLICATIONS AND ANALYTICS
8 Fog Computing in Healthcare: Application Taxonomy, Challenges and Opportunities
8.1 Introduction
8.2 Research Methodology
8.3 Application Taxonomy in FC-Based Healthcare
8.4 Challenges in FC-Based Healthcare
8.5 Research Opportunities
8.6 Conclusion
References
9 IoT-Driven Predictive Maintenance Approach in Industry 4.0: A Fiber Bragg Grating (FBG) Sensor Application
9.1 Introduction
9.2 Review of Related Research Articles
9.3 Research Gaps
9.4 Emerging Research Directions
9.5 The Broad Concept of FBG Sensor Applications in Industry 4.0
9.6 Conclusion
References
10 Fog Computing-Enabled Cancer Cell Detection System Using Convolution Neural Network in Internet of Medical Things
10.1 Introduction
10.2 Fog Computing: Approach of IoMT
10.3 Relationship Between IoMT and Deep Neural Network
10.4 Fog Computing Enabled CNN for Medical Imaging
10.5 Algorithm Approach of Proposed Model
10.6 Result and Analysis
10.7 Conclusion
References
11 Application of IoT in Smart Farming and Precision Farming: A Review
11.1 Introduction
11.2 Methodologies Used in Precision Agriculture
11.3 Contribution of IoT in Agriculture
11.4 IoT Enabled Smart Farming
11.5 IoT Enabled Precision Farming
11.6 Machine Learning Enable Precision Farming
11.7 Application of Operational Research Method in Farming System
11.8 Conclusion
11.9 Future Scope
References
12 Big IoT Data Analytics in Fog Computing
12.1 Introduction
12.2 Literature Review
12.3 Motivation
12.4 Fog Computing
12.5 Big Data
12.6 Big Data Analytics Using Fog Computing
12.7 Conclusion
References
13 IOT-Based Patient Monitoring System in Real Time
13.1 Introduction
13.2 Components Used
13.3 IoT Platform
13.4 Proposed Method
13.5 Experimental Setup and Result
13.6 Conclusion
References
14 Fog Computing and Its Emergence with Reference to Applications and Potentialities in Traditional and Digital Educational Systems: A Scientific Review
14.1 Background
14.2 Objectives
14.3 Methods
14.4 Fog Computing: Basics and Advantages
14.5 Growing Fog Computing Applications Emphasizing Education
14.6 Impact of Fog Computing in Education
14.7 Education Industry and Fog: Future Context
14.8 Fog Computing and Its Role in IOT Security: The Context of Campus
14.9 Concluding Remarks
References
Part III: SECURITY IN FOG COMPUTING
15 Blockchain Security for Fog Computing
15.1 Introduction
15.2 State of the Art
15.3 Security Issues in the Fog Computing Environments
15.4 Blockchain Technology
15.5 Blockchain Security for Fog Computing Environment
15.6 Summary and Conclusion
References
16 Blockchain Security for Fog Computing and Internet of Things
16.1 Introduction
16.2 Pros and Cons of Blockchain
16.3 The Properties of Blockchain
16.4 The Attacks on Blockchain
16.5 Application of Blockchain Technology in Healthcare
16.6 Fog Computing
16.7 Confidentiality Concerns in Fog Computing
16.8 Cloud Computing Security
16.9 Fog Computing Security Breaches
16.10 Optimized Fog Computing
16.11 Open Research Issues in Blockchain and Fog Computing Security
16.12 Conclusion
References
17 Fine-Grained Access Through Attribute-Based Encryption for Fog Computing
17.1 Introduction
17.2 Attribute-Based Encryption
17.3 Fine-Grained Access Through ABE
17.4 ABE Model for Fine-Grained Access
17.5 Application of ABE on Fog Computing
17.6 A Comparison of ABE Scheme
17.7 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Difference between cloud computing and fog computing.
Table 1.2 Difference between fog computing and IoT.
Chapter 2
Table 2.1 Comparative discussion among different computation offloading techni...
Chapter 4
Table 4.1 Assessment of multiple ML algorithms’ accuracy.
Chapter 6
Table 6.1 The pandemic alert system of the World Health Organization (WHO) has...
Table 6.2 Data table names.
Chapter 7
Table 7.1 Applications of machine-learning algorithms in medical data analysis...
Chapter 8
Table 8.1 Summarization of layered activities in fog computing.
Table 8.2 Research question.
Table 8.3 Summary of review papers on FC-based healthcare.
Table 8.4 Recent publications on diagnosis in FC-based healthcare.
Table 8.5 Recent publications on monitoring in FC-based healthcare.
Table 8.6 Recent publications on notification in FC-based healthcare.
Chapter 9
Table 9.1 Literature review of related research articles.
Table 9.2 Equipment and processes monitored by vibration analysis.
Chapter 11
Table 11.1 Contribution of IoT in agriculture.
Table 11.2 List of smart farming systems with functional sensors and communica...
Table 11.3 Contribution of IoT in precision farming.
Table 11.4 List of nutrients in soil.
Table 11.5 Depicts the present scenario of soil properties control using ML-ba...
Chapter 13
Table 13.1 LM35 sensor specifications.
Table 13.2 Computation of errors of actual data and observed data.
Table 13.3 Comparison with other techniques.
Chapter 15
Table 15.1 Trust and authentication techniques in fog computing.
Table 15.2 Data protection, privacy, and access control techniques in fog comp...
Chapter 17
Table 17.1 Comparison table of ABE schemes [3].
List of Illustrations
Chapter 1
Figure 1.1 Fog computing characteristics.
Figure 1.2 Fog computing architecture.
Figure 1.3 Basic modules of fog computing.
Figure 1.4 Applications of fog computing.
Chapter 2
Figure 2.1 Clone-cloud-based framework.
Figure 2.2 Phone2Cloud architecture.
Chapter 3
Figure 3.1 Fog computing in Industry 4.0.
Figure 3.2 Application areas of fog computing.
Chapter 4
Figure 4.1 The flow diagram of the proposed framework.
Figure 4.2 Dataset parameters.
Figure 4.3 Univariate distribution of parameters on the data set 1.
Figure 4.4 Univariate distribution of N-P-K values.
Figure 4.5 Heatmap on dataset 1 parameters.
Figure 4.6 Heatmap on dataset 2.
Figure 4.7 Line plot for N_P_K feature visualization.
Figure 4.8 Crop-wise feature visualization.
Figure 4.9 Comparison of model’s performance.
Chapter 5
Figure 5.1 Procedure movement of sensor to cloud.
Figure 5.2 Various categories of cloud computing.
Figure 5.3 Flaws of fog computing.
Figure 5.4 Advantages of fog computation.
Figure 5.5 Limitations of fog computing.
Figure 5.6 Fog calculation with IoT.
Figure 5.7 Software characteristics in fog computing.
Figure 5.8 Model of clever agriculture.
Figure 5.9 Some key obstacles to IoT cloud computing.
Chapter 6
Figure 6.1 Integration design.
Figure 6.2 High-level process flow.
Figure 6.3 Solution flow.
Figure 6.4 Landing page.
Figure 6.5 Helping information.
Figure 6.6 Symptom detection.
Figure 6.7 Testing centers for COVID-19.
Figure 6.8 Hospital information.
Figure 6.9 Oxygen supplier’s information.
Figure 6.10 COVID cases information.
Figure 6.11 Vaccination information.
Figure 6.12 Patients’ information.
Chapter 7
Figure 7.1 Types of machine learning algorithms.
Figure 7.2 Types of clustering algorithms [35].
Chapter 8
Figure 8.1 Relationship between cloud computing, fog computing, and Internet o...
Figure 8.2 Essential characteristics of fog computing (FC).
Figure 8.3 Sketch of review process.
Figure 8.4 Number of publications made by the publishers.
Figure 8.5 Application taxonomy in FC-based healthcare.
Figure 8.6 Major challenges in FC-based healthcare system.
Figure 8.7 A chronological view of research opportunities in FC-based healthca...
Chapter 9
Figure 9.1 Broad concept of Industrial Revolution and Industry 4.0.
Figure 9.2 Snapshot of a prototype version of a CMS. [Source: Cakir
et al
. (20...
Figure 9.3 Procedure to evaluate CMS model built using R-studio environment. [...
Figure 9.4 Multiple FBGs are embedded inside an MR fluid container. [Source: L...
Figure 9.5 The sensor units comprised FBG and Terfenol-D with opposing magneti...
Figure 9.6 The configuration of optical fiber current sensors fabricated using...
Figure 9.7 A dual-parameter sensor for both magnetic field and temperature. [S...
Figure 9.8 A broad understanding of FBG sensing in Industry 4.0.
Chapter 10
Figure 10.1 Fog computing block diagram.
Figure 10.2 Wings of fog computation application.
Figure 10.3 CNN-based fog computation model.
Figure 10.4 Deep learning based algorithmic approach model.
Figure 10.5 Result image of DGMM computation model.
Chapter 11
Figure 11.1 Flowchart of conventional farming.
Figure 11.2 Different applications of IoT in agriculture humidity monitoring....
Figure 11.3 Google survey on IoT and sensor-based agriculture. [Courtesy: M. T...
Figure 11.4 Generalized model of machine learning.
Figure 11.5 Different machine learning algorithms.
Figure 11.6 ML-based agriculture system.
Figure 11.7 Basic flow diagram of OR method to solve real world problems [59].
Chapter 12
Figure 12.1 Fog computing in larger perspective.
Figure 12.2 Fog node architectural service model.
Figure 12.3 Fog computing architecture.
Figure 12.4 Data and control flow for layered fog computing architecture.
Figure 12.5 Characteristic of big data.
Figure 12.6 A typical big data analytic flow diagram.
Figure 12.7 Big data analytic using fog-engine.
Chapter 13
Figure 13.1 Flow diagram of the database search, selection, and the review pro...
Figure 13.2 Node MCU.
Figure 13.3 ESP-12E module in Node MCU.
Figure 13.4 Power requirements in Node MCU.
Figure 13.5 Peripherals and I/O.
Figure 13.6 On-board switches and LED indicators.
Figure 13.7 CP2102 in Node MCU.
Figure 13.8 Complete pin diagram of the Node MCU.
Figure 13.9 Pulse rate sensor.
Figure 13.10 Hardware diagram of a heart rate sensor.
Figure 13.11 Op-Amp and reverse protection diode.
Figure 13.12 Working of a pulse sensor.
Figure 13.13 Working of a pulse sensor.
Figure 13.14 Pin diagram of the pulse sensor.
Figure 13.15 LM35.
Figure 13.16 Temperature vs. I
c
collector current.
Figure 13.17 Temperature vs. I
c
collector current.
Figure 13.18 ThingSpeak.
Figure 13.19 Working process of ThingSpeak.
Figure 13.20 Flow diagram of the proposed technique.
Figure 13.21 Arduino programming IDE.
Figure 13.22 Creating channel fields.
Figure 13.23 Write API key.
Figure 13.24 Implemented online virtual schematic.
Figure 13.25 (a) Hardware connection and (b) checking vitals real time.
Figure 13.26 Measured real-time data of patient temperature.
Figure 13.27 Measured real-time data of patient pulse rate.
Chapter 14
Figure 14.1 Basic fog computing architecture.
Figure 14.2 Fog computing and in-contrast with end devices.
Figure 14.3 Fog computing advantages and its beneficiaries.
Figure 14.4 Fog computing potential utilization in education and allied areas....
Figure 14.5 Certain fog computing issues in education and allied areas.
Chapter 15
Figure 15.1 Fog computing structure [2].
Figure 15.2 Security attack issues in fog computing.
Figure 15.3 Structure of blockchain.
Figure 15.4 Blockchain layered architecture.
Figure 15.5 Blockchain in fog computing.
Chapter 16
Figure 16.1 Steps in block chain information and transactions [21].
Figure 16.2 Supply chain transformation through blockchain [21].
Figure 16.3 Several types of block chain and trust levels [30].
Figure 16.4 The structure of the smart contract [33].
Figure 16.5 Cisco IOx lifecycle and security pillars.
Chapter 17
Figure 17.1 Symmetric-key cryptography.
Figure 17.2 Asymmetric-key cryptography.
Figure 17.3 Attribute-based encryption.
Figure 17.4 Fine-grain data access control.
Figure 17.5 ABE in fog computing.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
Pages
ii
iii
iv
xvii
xviii
xix
xx
xxi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Series Editor: Dr. Souvik Pal and Dr. Dac-Nhuong Le
The role of adaptation, learning analytics, computational Intelligence, and data analytics in the field of Cloud-IoT Systems is now intertwined. The capability of an intelligent system depends on various self-decision making algorithms in IoT Devices. IoT based smart systems generate a large amount of data (big data) that cannot be processed by traditional data processing algorithms and applications. Hence, this book series involves different computational methods incorporated within the system with the help of analytics reasoning and sense-making in big data, which is centered in the cloud and IoT-enabled environments.
The series seeks volumes that are empirical studies, theoretical and numerical analysis, and novel research findings. The series encourages cross-fertilization of highlighting research and knowledge of data analytics, machine learning, data science, and IoT sustainable developments.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Fog Computing for Intelligent Cloud IoT Systems
Edited by
Chandan Banerjee
Netaji Subhash Engineering College, India
Anupam Ghosh
Netaji Subhash Engineering College, India
Rajdeep Chakraborty
Chandigarh University, India
and
Ahmed A. Elngar
Beni Suef University, Egypt
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-3941761-4
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is also known as edge fog networking, or fog networking, or fogging. Fog computing is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources.
Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user’s experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service.
Fog computing has various applications across industries, such as agriculture and farming, healthcare industry, smart cities, education, and entertainment. For example, in agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing’s help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage.
This book is divided in to three parts. The first part studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent cloud-IoT system, machine learning fundamentals, and data visualisation. The second part focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This part also covers analytics in fog computing using big data and patient monitoring system, and the emergence of fog computing with reference to applications and potentialities in traditional and digital educational systems. Last but not least, the third part covers security aspects in fog computing through blockchain and IoT, and fine-grained access though attribute-based encryption for fog computing.
Part I: Study of Fog Computing and Machine Learning
Chapter 1 starts with an overview and introduction to fog computing characteristics followed by a brief description of fog computing’s application in intelligent Cloud-IoT systems. Then a detailed fog computing architecture is given, with descriptions of basic modules in this architecture. Following that, a relative comparison of cloud computing and fog computing is drawn by examining the applications of fog computing. The chapter ends with a summary of challenges faced by, and the future scope of fog computing.
Chapter 2 discusses how computation offloading is a critical technology in the rapidly developing field of Mobile Cloud Computing (MCC). MCC can improve application speeds, reduce latency, and extend battery life. Among other things, the effect of computation offloading is influenced greatly by a variety of parameters. After a short introduction and summary of related work, this chapter leads with different computation offloading techniques. The chapter ends with related studies and comparisons of these offloading techniques, along with the future scope of research in mobile offloading.
Chapter 3 explores the optimized and green uses of fog computing in Industry 4.0. After an introduction to Industry 4.0 and fog computing, this chapter focuses on how IoT integration with fog computing will have wide application in Industry 4.0. This chapter also provides two fog computing architectures, hierarchical and layered. This chapter ends with descriptions of various applications for fog computing, plus data analysis with related figures and tables.
Chapter 4 elaborates upon machine learning and its integration in agriculture. This chapter starts by demonstrating the importance of integrating machine learning solutions into agriculture. Then it proceeds to demonstrate fog computing as a backbone for collecting and filtering agricultural data. Afterward, a proposed model is given as methodology, followed by a discussion of the results and various modelling algorithms, such as Decision Trees, Random Forest, XGBoost, CatBoost and LightGBM.
Chapter 5 discuss how smart buildings utilize Internet of Things (IoT) devices, sensors, software, and internet connectivity to monitor various aspects of the building, analyse data, and extract insights to enhance the building’s environment and operations, i.e., the role of intelligent IoT applications in fog computing. Following that is a detailed discussion of fog and cloud computing, and fog computing with IoT, Edge, AI, and other network intelligence objectives. The chapter concludes with case studies.
Chapter 6 gives a SaaS-based data visualization platform in Covid-19 perspective. The chapter starts by defining the pandemic in terms of data visualization and Power BI, followed by a summary of data collection and wrangling. A proposal for design and implementation are given, followed by dashboard development. The chapter ends by addressing advantages, impact, and future scope.
Chapter 7 is a complete study of machine learning algorithms for medical data analysis, particularly COVID–19. After an introduction, this chapter explains pre-processing of medical data for machine learning, followed by supervised learning and Support Vector Machine (SVM). Next, Naive Bayes Algorithm and KNN are covered. Thereafter, the chapter covers deep learning algorithms with illustrations of unsupervised learning, and applications of ML algorithms in medical data analysis. All are illustrated with medical data analysis. The chapter ends by addressing future research scope and challenges.
Part II: Applications and Analytics
Chapter 8 illustrates an application of fog computing in healthcare industry. The chapter starts by presenting a layered architecture of fog computing in detail, followed by research methodologies. Then this chapter gives an application taxonomy of fog computing-based healthcare system with disease diagnosis, monitoring, and notification. The chapter ends by considering the challenges and research opportunities of a fog computing-based healthcare system.
Chapter 9 gives an IoT-driven predictive maintenance approach to industry 4.0 with a Fiber Bragg Grating (FBG) sensor application. After a detailed introduction, different ML algorithms are reviewed, such as LDA, DT, kNN, SVM, and RF. Thereafter, research gaps are highlighted with emergent research directions. The chapter ends by covering the broad concept of FBG sensor applications in Industry 4.0.
Chapter 10 proposes a fog computing enabled cancer cell detection system using Convolution Neural Network in Internet of Medical Things (IoMT). After an introduction to fog computing in IoMT, the chapter discusses the relationship between IoMT and Deep Neural Network. The chapter then proposes a model of fog computing enabled CNN for Medical Imaging, followed by an algorithmic approach to proposed models. The chapter concludes with results and analysis.
Chapter 11 gives a detailed, application-based review of smart and precision farming. After the introduction, the chapter starts with methodologies used in precision agriculture, mainly map-based and sensor-based techniques and the contribution of IoT. Then a detailed study of IoT-enabled smart farming and precision farming is provided. Following that is an explanation of machine learning-based precision farming, illustrated with a case study of OR methods in farming systems. The chapter ends with conclusive remarks and a future scope.
Chapter 12 provides analytics of big data obtained from fog computing, with an introduction, literature review, and a summary of motivation. The chapter details fog computing architecture, followed by a thorough discussion on big data, then examines the big data analytics, and ends with concluding remarks.
Chapter 13 reviews the application of a IoT-based patient monitoring system in real time. After an introduction, the components used for this model are discussed, such as Node MCU, Heart Rate/Pulse Sensors, and Temperature Sensors (LM35), followed by a discussion of IoT platforms, such as ThingSpeak. A method is proposed, followed by instructions for experimental setup and results. The chapter concludes with the outcomes of this model and an explanation of how to use IoT-based patient monitoring system effectively in real time.
Chapter 14 is a scientific review of fog computing and its emergence, with reference to applications and potentialities in traditional and digital educational systems. The chapter starts with a background study, followed by a summary of the methods used for this research and the basics and advantages of fog computing. Following that is a report on the increase of fog computing applications and its impact on education and IoT security.
Part III: Security in Fog Computing
Chapter 15 illustrates the use of blockchain security for fog computing. This chapter gives thorough examples and outlines detailed security issues that present in fog computing environments. Following that is a detailed explanation of blockchain architecture, further discussion of the topic, and summary conclusions.
Chapter 16 covers an extension of blockchain security in IoT with fog computing. This chapter starts with a detailed discussion about the types of blockchain, and its pros and cons. Then the chapter discusses the properties of blockchain like PoW, PoS, smart contracts, and blockchain attacks, such as the 51% attack, double spending, Sybil’s attack, and DDoS attack.
After that, some major application areas of blockchain are discussed. The chapter then addresses confidentiality concerns in fog computing, and how it can be solved using blockchain. Security breaches in cloud computing and fog computing is are also described, as well as how it can be solved using blockchain. The chapter ends with open research issues in blockchain and fog computing security, such as scalability, privacy and anonymity, consensus mechanisms, fog computing security, smart contract security, and interoperability.
Chapter 17 is about Attribute-Based-Cryptography (ABE), and its application for fine-grained access in fog computing. After an introduction to the basics of public key cryptography, this chapter discusses ABE in detail, followed by how fine-grained access through ABE can be done on fog computing. Then an example of how an ABE model for fine-grained access can be used in fog computing is given, followed by a demonstration of how ABE can be applied to fog computing. The chapter ends with comparisons of few existing ABE schemes, such as KP-ABE, CP-ABE, H-ABE, and MA-ABE.
We are deeply grateful to everyone who helped with this book and greatly appreciate the dedicated support and valuable assistance rendered by Martin Scrivener and the Scrivener Publishing team during publication.
The EditorsMarch 2024
Part ISTUDY OF FOG COMPUTING AND MACHINE LEARNING
1Fog Computing: Architecture and Application
Soumen Swarnakar
Department of Information Technology, Netaji Subhash Engineering College, Kolkata, India
Abstract
Nowadays, owing to the advancement of electronics and telecommunications and the increasing usage of the Internet, various powerful devices with networking capabilities are attracting industries to accept this technology for the development of their daily business to increase their productivity. Not only the industrial sector but also other sectors, such as public services and assisted living amenities have a huge demand for Information and Communication Technology growth. Therefore, there is a demand for a new model that enables objects to connect to global network. This model is known as the IoT. The use of the IoT has grown according to the demands of the current era. IoT combinations with clouds have brought different benefits to devices that operate on various platforms. Applications based on the IoT generate enormous amounts of data from different sensors. These data were used to make different decisions.
It is true that cloud computing has some issues in data transmission owing to the limitations of networks and infrastructure, which immensely decrease the performance of cloud computing. Therefore, the fog computing model was introduced to act in the middle between cloud computing and IoT. Fog is an extended version of cloud computing that can provide computing facilities at the edge of the network, and it allows this technology to deal with several data locally. As devices connected to the Internet and the use of the Internet of Things (IoT) continue to grow, the need for efficient and scalable computing solutions has become paramount. Fog computing has emerged as a model that extends the features of cloud computing to the network edge, bringing storage, computation, and networking closer to end users and devices.
This article presents an in-depth analysis of the architecture, applications, and challenges of fog computing. It also discusses the fundamental concepts of fog computing, including its architectural components. Fog computing has been compared with IoT and cloud computing. Furthermore, it explores the different uses of fog computing in different domains, such as healthcare, smart cities, transportation, manufacturing, and agriculture. This paper also examines the different challenging features and future study guidelines in the field of fog computing.
Keywords: IoT, cloud computing, edge computing, fog computing, smart city, sensor, edge nodes
1.1 Introduction
This fog computing can be considered as the computation of the next generation which is an extended version of cloud computing towards the edge of the network. Therefore it is also known as edge computing [16]. Issues faced by cloud computing can be addressed using fog computing.
Currently, the volume of data generation is exploding owing to the vast quantity of data generated daily by sensors, IoT devices, wide volume of internet usage, and so on. Fog computing can report the impractical features of a cloud that cannot fulfill the demands of users in real time. IoT applications, the Industrial Internet of Things, and the Internet of Everything, etc. are different reasons for the motivation or interest in the application of fog computing. Currently, data are one of the most important and essential factors for fulfilling and maintaining existence in today’s era, and the usage of fog computing is spreading rapidly across organizations. Fog computing has gained significant attention as a promising paradigm for extending cloud-computing capabilities to network edges. While fog computing offers numerous benefits, such as reduced latency, improved scalability, and enhanced data privacy, it also presents several challenges that need to be addressed. Resource allocation is becoming an issue with respect to IoT application in fog computing [1, 2]. Several features of the different methods for allocating resources are used in fog computing. The objective of this article is to study the latest research on the allocation of resources in the fog area and to conduct a comparative study with cloud and IoT with different applications of fog computing. The justification of this study is to understand the architecture and application of fog computing and to provide a clear idea of the future applications of fog computing. From this article, researchers will benefit from a better understanding and utilization of this technology. Soon, different IoT devices will occupy the world because of the significant increase in the usage of different objects connected by different sensors connected by the Internet [3].
In this study, the remainder is divided into different sections. Section 1.2 presents an overview of Fog Computing, Section 1.3 describes fog computing for intelligent cloud IoT systems, Section 1.4 discusses the architecture of fog computing, Section 1.5 presents the basic module of fog computing, Section 1.6 presents the differences between cloud computing and fog computing, Section 1.7 discusses the differences between fog computing and IoT systems, Section 1.8 presents fog computing applications, Section 1.9 discusses whether fog computing can be taken over by cloud computing, Section 1.10 presents the challenges in fog computing, Section 1.11 discusses the future of fog computing, and finally, the conclusion is drawn in Section 1.12.
1.2 Fog Computing: An Overview
Cloud and fog computing are interrelated. In 2014, CISCO created the word fog computing, so obviously it is a new term for common human beings. As we know, fog exists closer to Earth than clouds, and the same is applicable in technical terms. Cloud capabilities are easier to use using fog computing.
Therefore, fog is an extension technology of cloud computing, which consists of several edge nodes connected with physical devices.
These nodes are much closer to devices if related to centralized data centers, which is why they can provide prompt connections. The computation power of edge nodes permits them to execute the processing of an enormous amount of data generated without transferring it to servers at a distance. Fog computing, also known as fog networks or edge computing, is an emerging paradigm that extends cloud-computing capabilities to the edge of a network. It brings computing resources, including storage, processing power, and networking capabilities closer to the data source or end-user devices. Fog computing offers several significant benefits, which contribute to its increasing importance in various domains.
Fog computing [8] can be considered as a decentralized computing arrangement. Here, decentralized indicates that computing resources are not fixed. Computing resources in fog computing will be moved and fixed closer to the IoT devices that generates records. Thus, this idea reduces the latency and network bandwidth usage, which can result in faster computation. Different large organizations have recently utilized fog computing to compute faster.
One of the important factors is the fog node. These are the virtual instances existing in the fog layer, which are used to provide extra benefits to the process of cloud computing. A fog node is a group of computing resources related to a specific sector. A small storage component acts similarly to a fog node. The fog layer is linked with a big cloud that may be a private or public cloud from which the fog nodes fetch records and push the record to it. Fog nodes can be used to provide transient storage ability. If the data are not transient, then it is relocated to the cloud to store the data.
Fog nodes rapidly agree whether to compute a record or send the record to the cloud for computation.
Each fog node has a comprehensive fog node and can be deployed over the entire network. Examples: routers, switches, etc.
The fog consists of cloudlets, which are small-scale datacenters situated at the edge of the network. Their main objective is to support resource-intensive Internet of Things applications that require low latency.
Fog computing services are very close to end devices. Because it is closer to end devices, the proposed computing model has important benefits over other conventional computing models. The important features of this study are as follows:
Geographical distributionFog nodes are widely distributed geographically [
4
]. These were positioned at different locations. For example, they are located on different positions of roads and highways, on the museum floor, and so on.
DecentralizationFog computing has a decentralized architecture. No central server is available for managing different computing resources and facilities. Therefore, fog nodes are self-organizing and work together to provide a facility for real-time IoT applications to end users.
Location awarenessThe ability to determine the geographical location of a device is called location awareness. The fog node is linked to the nearby fog node, and it knows the location of the fog client. Location awareness can be used in emergency conditions or targeted advertising.
Real-time interactionThe fog application supports real-time interaction instead of batch processing. Real-time stream handling and gaming are used in fog computing. Because fog is very close to the edge, it offers healthy network information on the local network, traffic information, and status information.
Save storage spaceFog computing reduces latency and storage space [
21
,
22
] consumption by avoiding inopportune data from moving across the entire network.
Low latencyThe time taken for data to move from the source device to the server device is called the latency. By placing computing resources near the edge, the network latency of fog computing can be significantly reduced. This is crucial for applications that require real-time or near-real-time responsiveness, such as Internet of Things (IoT) devices, autonomous vehicles, industrial automation, and healthcare systems. In fog computing, latency is lower because the data do not have to move far away from the device. In the IoT and Cloud model, data generated by sensors are moved to the data center of the cloud, which is situated far from the IoT devices. Therefore, end-to-end delay occurs, such as deferral of data sent from IoT devices to data centers situated remotely, the delay of investigating the data, and the response coming back is delayed by the cloud to the end user. Therefore, cloud computing has a high latency. The nodes of fog computing are much closer to IoT devices [
5
], which provide different computing facilities, and decisions are made based on local data without the use of the cloud. Therefore latency in response is comparatively much lower [
4
] than that of cloud computing. With fog computing, data processing and decision making can occur locally, enhancing the overall user experience and enabling faster response times.
Heterogeneity supportThe fog computing model has various types of nodes, such as set-top boxes, edge routers, high-end servers, and access factors, and even end nodes. Examples include cellular phones, automobiles, and sensors. They have received high-speed servers, edge routers, etc., operated via an operating device with special storage capabilities and computational electricity. It also provides virtual network platform. As different computing nodes and nodes of a virtual network can be used as fog nodes, fog nodes are heterogeneous in nature.
Mobility supportUsing different protocols, mobility support is an important characteristic for different fog computing applications to communicate directly with movable devices. We know the ID separation protocol developed by CISCO, which can use a directory system that separates the identity of the host from identity of the site.
Close to the end userTo remove delays [
24
] in data communication, fog permits data to be closer to users than in data centers kept in remote locations.
Bandwidth optimizationFog computing optimizes the network bandwidth [
20
] by processing data at the edge rather than transmitting it to centralized cloud servers. This is particularly valuable in scenarios in which IoT devices or sensors generate massive amounts of data. By locally performing data filtering, preprocessing, and aggregation, fog computing reduces the amount of data transmitted to the cloud. This alleviates network congestion and minimizes bandwidth costs.
Enhanced privacy and securitySensitive data can be processed and stored locally in fog computing, whereas data can be sent to a remote cloud server in cloud computing. This distributed approach to computing enhances privacy and security [
9
,
14
,
27
] because data can be kept closer to its source and subject to localized security measures. It reduces the risk of data breaches and unauthorized access and ensures compliance with privacy regulations.
Improved reliabilityFog computing increases the reliability and resilience of networked systems. Distributing computing resources across a network mitigates a single point of failure. If a connection to the cloud is lost, fog nodes can continue to function independently and maintain critical operations. This is particularly critical for applications in which uninterrupted connectivity is crucial, such as autonomous, industrial control, and emergency response systems.
Scalability and agilityFog computing enables scalability and agility in the deployment and management of distributed applications. As the number of edge computing nodes and IoT devices increases, fog computing allows for easy resource scaling by adding more edge devices. It also enables the dynamic provisioning of computing resources based on demand, allowing applications to adapt and respond efficiently to changing workload requirements.
Cost optimizationBy offloading computational tasks to edge devices, fog computing reduces the need for high-end servers and an expensive cloud infrastructure. It can leverage existing computing resources at the edge, such as routers, gateways, and IoT devices, thereby optimizing resource utilization and reducing operational costs. Fog computing enables cost-effective solutions for deploying and managing distributed applications, particularly in scenarios in which large-scale cloud infrastructure is not feasible or cost prohibitive.
Fog computing plays an important role in enabling effective and reliable computations at the network edge. This has important advantages with respect to reduced latency, optimized bandwidth, enhanced privacy and security, improved reliability, scalability, agility, and cost optimization. As the demand for real-time and edge-centric applications grows, fog computing is becoming increasingly relevant in various domains, including IoT, industrial automation, smart cities, healthcare, and transportation. The characteristics of fog computing are summarized in Figure 1.1.
Figure 1.1 Fog computing characteristics.
Fog computing can be considered situated between remote servers and hardware. Therefore, it is easier to decide and control the sending of data to a server or executed locally. A fog serves as a smart gateway that offloads cloud computing, allowing for more effective data processing, storage, and analysis [22].
Decentralization of computing infrastructure is the main factor in fog computation. Here, a flexible computing infrastructure consisting of data, storage, and applications is situated somewhere between the cloud and data source, which is a data generator. Similar to edge computing [16], fog computing reduces the distance between the data source and the location where it is executed. Edge computing and fog computing concepts are nearly the same, as both technologies bring computation and processing closer to the data generation location. Sometimes, it is done to increase efficiency, as well as for compliance and security reasons.
Currently, low-cost sensors are helping to increase the usage of IoT devices. IoT can be implemented successfully when the following requirements are met:
Reliability of data.
High data security
Reduction in latency of data
Based on the data type, the execution of data at the appropriate location.
To monitor data through large geographical area
Fog computing meets all the above requirements for IoT networks.
1.3 Fog Computing for Intelligent Cloud-IoT System
Fog computing is an architectural concept that extends cloud computing capabilities to the edge of the network, closer to the data sources in an Internet of Things (IoT) ecosystem [9]. It aims to address the limitations of traditional cloud-centric architectures by leveraging the computational resources available at network edges, such as gateways, routers, and IoT devices. This enables faster processing, reduced latency, improved bandwidth utilization, and enhanced privacy and security in cloud-based IoT systems. When combined with intelligent capabilities, fog computing can further enhance the performance and efficiency of cloud-IoT systems. Some concepts related to fog computing for intelligent cloud-IoT systems are as follows:
Edge Intelligence: Fog computing enables intelligent processing and decision-making at the edge of the network. By deploying machine learning algorithms and artificial intelligence models on edge devices, data can be analyzed and acted upon in real time without the need to send it to the cloud. This reduces the latency and enables quicker response times for time-sensitive applications.
Distributed Data Analytics: Fog computing allows data analytics tasks to be distributed across clouds and edge devices. By partitioning data processing and analysis between the cloud and edge, it is possible to strike a balance between computational capabilities and network-bandwidth usage. This approach optimizes overall system performance and reduces the need for extensive data transfer to the cloud.
Dynamic Resource Allocation: Fog computing enables intelligent resource allocation and management in cloud-IoT systems. By leveraging edge devices’ computational capabilities, tasks can be dynamically assigned to the most suitable resources based on factors like proximity, network conditions, and resource availability. This adaptive resource allocation ensures efficient utilization of computing resources and improved system scalability.
Context-Awareness: Fog computing enhances the context-awareness of cloud-IoT systems by leveraging data from sensors and devices at the edge. Contextual information such as location, environmental conditions, and user preferences can be processed locally to provide personalized services, real-time monitoring, and better decision-making. This localized processing minimizes the need for constant communication with the cloud, thereby reducing latency and conserving the network bandwidth.
Security and Privacy: Fog computing improves security [
17
,
27
] and privacy in cloud-IoT systems by keeping sensitive data closer to its source. Instead of transmitting all the data to the cloud for processing, fog nodes can perform data filtering, aggregation, and anonymization at the edge. This approach reduces the attack surface and risk of data breaches, making the system more resilient to cyber threats.
Hybrid Cloud–Fog Collaboration: Fog computing can be seamlessly integrated with cloud computing to create a hybrid cloud–fog architecture. This collaboration allows for the offloading of resource-intensive tasks to the cloud, while maintaining time-sensitive and critical operations at the edge. The hybrid model provides the benefits of both cloud and fog computing, optimizing the trade-off between scalability, agility, and latency in cloud IoT systems.
These concepts demonstrate how fog computing can enhance cloud-IoT systems by providing intelligence, real-time processing, and efficient resource management to the network edge. By leveraging the strengths of both cloud and fog computing, organizations can build scalable, responsive, and secure IoT ecosystems that can fulfill the demands of a large variety of applications.
1.4 Fog Computing Architecture
Fog computing architecture is generally composed of three different working layers. There are three working layers in the fog computing architecture [6, 8, 25]: the edge layer, fog layer, and cloud layer. The three-layer architecture of fog computing is described below and presented in Figure 1.2.
Figure 1.2 Fog computing architecture.
Cloud Layer:
Cloud layer represents the centralized cloud infrastructure at the top of the architecture. It provides storage, computing power, and advanced analytics capabilities for fog computing deployment.
The cloud layer is responsible for handling resource-intensive tasks, storing large datasets, and supporting long-term data analyses.
Fog Layer:
The fog layer is located closer to the network edge and comprises a distributed network of fog nodes.
Fog nodes are interconnected devices that include edge servers, routers, gateways, and IoT devices.
These nodes are responsible for processing, analyzing, and storing data locally, thereby reducing latency and network congestion.
Fog nodes can be deployed at various levels of the network hierarchy, such as near end-devices, within access points, or at aggregation points.
Edge Layer:
The edge layer consists of edge devices representing sensors, actuators, and IoT devices that generate data and interact directly with the physical world.
These devices are responsible for collecting and transmitting data to fog nodes for local processing and analysis.
Communication Infrastructure:
Communication infrastructure connects fog nodes and edge devices, facilitating data exchange and communication.
It can include wired and wireless networks such as Ethernet, Wi-Fi, cellular networks, and other IoT-specific protocols.
Data Processing and Analytics:
The fog nodes within the fog layer perform data processing, analytics, and decision-making tasks.
This layer can utilize various techniques, including machine learning algorithms, stream processing, and real-time analytics, to extract insights from data.
Resource Management and Orchestration:
Resource management and orchestration mechanisms optimize the allocation and utilization of computing, storage, and networking resources within the fog layer.
This involves tasks such as load balancing, task scheduling, and dynamic resource provisioning to ensure efficient resource utilization.
Security and Privacy:
The security and privacy layers focus on ensuring the confidentiality, integrity, and availability of data within the fog computing environment.
This includes encryption mechanisms, access control, authentication, and intrusion detection systems to protect data and preserve user privacy.
Cloud–Fog Integration:
Cloud–fog integration enables seamless communication and collaboration between cloud and fog layers.
It allows fog nodes to offload certain tasks to the cloud, access additional resources when needed, and enable the long-term storage and analysis of data.
It is important to note that fog computing architectures can vary depending on specific use cases and deployment scenarios. The described architecture provides a general overview of the components and their relationships within a fog-computing environment.
Different types of fog computing exist, such as client-based, server-based, and hybrid fog computing.
Client-Based Fog:
It is based on the computing power of edge devices to execute and analyze data. Client-based fog computation is best for applications that require real-time processing, such as industrial IoT and autonomous vehicles.
Server-Based Fog:
It is based on the computing power of servers located in the fog layer to execute and analyze data. Server-based fog computation is best for applications that require extra computing power compared with edge devices.
Hybrid Fog:
This fog computation is a mixture of client-based and server-based fog computations. Hybrid fog computing is perfect for applications that require a combination of high computing power and real-time processing.
1.5 Basic Modules of Fog Computing
There are several ways of creating a fog computing system. The well-known parts across these architectures are explained below and are summarized in Figure 1.3.
Physical and Virtual Nodes/End DevicesIn the real world, end devices function as points of interaction. They may be end devices, such as smart watches, module phone sensors, edge routers, application servers, and edge routers. These devices are record producers and can span a large band of technology. This means that they may have variable processing and storage capacities, and many hardware and software applications.
Fog NodesFog nodes are independent devices that collect the information produced. Fog nodes fall into three categories: gateways, fog servers, and fog devices. These devices can store the necessary data, whereas fog servers can compute their data to take the course of action. Fog devices are generally connected to servers. The fog gateways redirect information between several fog devices and servers. This layer is responsible for the speed handling and flow of information.
Monitoring ServicesMonitoring services usually include application programming interfaces or APIs that keep track of how the system will perform and the availability of resources. This guarantees that all fog nodes and end devices are active and communication is not delayed [
24
]. It is observed that if we wait for a node to be free, then it may be comparatively more costly than to hit the cloud server. The monitor manages such situations. Monitors can be used to assess the present system and predict future resource necessities based on their overall usage.
Figure 1.3Basic modules of fog computing.
Data ProcessorsData processors play an important role in fog computing. The function is to operate on fog nodes. They trim, filter and occasionally rebuild damaged data that flow from end devices. Data processors can decide whether the data should be kept locally on a fog server or sent for long-term storage in the cloud. Data from different sources were homogenized for easy communication and transportation by these processors.This can be achieved by revealing a programmable and uniform interface with other modules in the system. If one or more sensors do not work, some data processors are so intelligent that they can fill the information based on old historical data. This can prevent any type of application failure.
Resource ManagerFog computing contains different independent nodes that can operate synchronously. The resource manager assigns and frees the resources to several nodes and schedules data transmission between the nodes and the cloud. It also considers the data backup to ensure zero data loss.As fog modules take up some of the SLA [
20
] promises of the cloud, great availability is necessary. The resource manager works with the monitor to decide where and when the demand is higher. This confirms that there is no redundancy in the fog servers or data.
Security ToolsFog components can directly interconnect with raw data sources. Encryption is necessary because all communication tends to occur over wireless networks. In some cases, end users directly ask fog nodes for data. Therefore, user and access management are part of safety efforts in fog computing.
1.6 Cloud Computing vs. Fog Computing
Fog computing and cloud computing are two distinct paradigms [10, 14] that serve different purposes in the realm of distributed computing. The key differences between fog computing and cloud computing are as follows:
Proximity to End Users and Devices:
Fog Computing: Fog computing brings computing resources closer to the network edge, end users, and IoT devices. It extends cloud-computing capabilities to the edge of the network, reduces latency, and improves real-time responsiveness.
Cloud Computing: Cloud computing, on the other hand, centralizes computing resources in data centers located at a distance from end users. Users access these resources on the internet.
Data Processing and Storage:
Fog Computing: In fog computing, data processing and storage occur at the network edge, close to where data are generated. This enables faster processing, reduced bandwidth usage, and localized decision making.
Cloud Computing: In cloud computing, data processing and storage occur in centralized datacenters. Users send data to the cloud for processing and the results are sent back to the user.
Scalability and Resource Allocation:
Fog Computing: Fog computing allows for horizontal scalability because fog nodes can be deployed in a distributed manner to handle increasing workloads. Resources can be dynamically allocated and managed at an edge.
Cloud Computing: Cloud computing enables vertical scalability, as additional resources are added to centralized data centers to handle increased demand. Resource allocation [
4
] and management are performed centrally.
Network Bandwidth and Latency:
Fog Computing: Fog computing reduces network bandwidth [
20
] requirements by processing and analyzing data locally at the edge and sending only relevant information to the cloud. This minimizes the latency and congestion.
Cloud Computing: Cloud computing relies on network connectivity for data transfer between the end user and the cloud, potentially leading to higher latency owing to data transmission over longer distances.
Use Case Focus:
Fog Computing: Fog computing is particularly suitable for latency-sensitive and real-time applications such as IoT, edge analytics, and time-critical systems. It is well suited for scenarios in which immediate decision-making and local data processing are crucial.
Cloud Computing: Cloud computing is commonly used in applications that require extensive computing resources, storage, and long-term data analysis. It is suitable for applications with less stringent latency requirements, where data processing can be offloaded to a centralized infrastructure.
Data Privacy and Security:
Fog Computing: Fog computing enhances data privacy and security by keeping sensitive data localized at the edge. Data can be processed and analyzed within a local network to reduce exposure to potential threats during data transmission.
Cloud Computing: Cloud computing involves transmitting data over the Internet to centralized data centers, raising concerns about data privacy and security during transmission and storage.
Table 1.1
